Specification for feature_group_strategies is not working with leave-one-out or leave-one-in

Hi everyone,

I'm running triage over **[Red Hat 11.3.1-4]** on Linux, Python 3.10.6, and using the v8 triage version. My database is in **PostgreSQL 15.7** on x86_64-pc-linux-gnu, compiled by GCC (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0, 64-bit.

Configuration details:

```
config_version: 'v8'

random_seed: 1472385

temporal_config:
feature_start_time: '2021-11-01'
feature_end_time: '2022-12-31'

label_start_time: '2021-11-01'
label_end_time: '2022-12-31'

model_update_frequency: '1month' # ventanas

max_training_histories: '6month' # periodo de entrenamiento
training_label_timespans: ['4d'] # tiempo en que puede suceder la etiqueta
training_as_of_date_frequencies: '1d' # cada cuando tomas la decision

test_durations: '1week' # cuanto tiempo usarás ese modelo
test_label_timespans: ['4d']
test_as_of_date_frequencies: '1d'

cohort_config: # Cohorte = Contenedores que llegaran a la terminal el siguiente día a partir del eta
filepath: 'triage/sql/cohorts/cohorte_antes_de_arribo.sql'
name: 'arribo_buque'

label_config: # Etiqueta = Si el contenedor saldrá entre 2 y 4 días
filepath: 'triage/sql/labels/label_2_4_dias_estadia.sql'
name: 'e2_4_dias'

feature_aggregations:
-
prefix: 'ecvr' # variables sencillas
from_obj: 'ontology.entities'
knowledge_date_column: 'fecha_eta'

aggregates_imputation:
all:
type: 'mean'

aggregates:
- # peso_neto
quantity: 'peso_neto'
metrics:
- 'max'
- # peso_bruto
quantity: 'peso_bruto'
metrics:
- 'max'

categoricals_imputation:
all:
type: 'null_category'

categoricals:
- # dimension
column: 'dimension'
metrics:
- 'sum'
choices: ['20','40','45']
- # ruta_linea_naviera
column: 'ruta_linea_naviera'
metrics:
- 'sum'
choice_query: 'select distinct ruta_linea_naviera from ontology.entities'

intervals: ['all']

-
prefix: 'mercha' # variables de mercancia
from_obj: 'ontology.comportamiento'
knowledge_date_column: 'fecha_eta'

categoricals_imputation:
all:
type: 'null_category'

categoricals:
- # capitulo
column: 'capitulo'
metrics:
- 'sum'
choice_query: 'select distinct capitulo from ontology.comportamiento'
- # seccion
column: 'seccion'
metrics:
- 'sum'
choice_query: 'select distinct seccion from ontology.comportamiento'

aggregates_imputation:
all:
type: 'mean'

aggregates:
- # conteo_capitulo_2sem
quantity:
ccap2s: 'conteo_capitulo_2sem'
metrics:
- 'min'
- # conteo_capitulo_4sem
quantity:
ccap4s: 'conteo_capitulo_4sem'
metrics:
- 'min'

intervals: ['all']

-
prefix: 'consig' # variables de consignatario
from_obj: 'ontology.comportamiento'
knowledge_date_column: 'fecha_eta'

categoricals_imputation:
all:
type: 'null_category'

categoricals:
- # consignatario top10
column: 'consignatario'
metrics:
- 'sum'
choice_query: 'with top50 as(select consignatario, count(consignatario) from ontology.comportamiento group by consignatario order by 2 desc limit 100) select consignatario from top50'

aggregates_imputation:
all:
type: 'mean'

aggregates:
- # conteo_consig_2sem
quantity:
ccons2s: 'conteo_consig_2sem'
metrics:
- 'min'
- # conteo_consig_4sem
quantity:
ccons4s: 'conteo_consig_4sem'
metrics:
- 'min'

intervals: ['all']

-
prefix: 'liru' # variables de linea y ruta contenedores
from_obj: 'ontology.comportamiento'
knowledge_date_column: 'fecha_eta'

aggregates_imputation:
all:
type: 'mean'

aggregates:
- # conteo_ruta_2sem
quantity:
crut2s: 'conteo_ruta_2sem'
metrics:
- 'min'
- # conteo_ruta_4sem
quantity:
crut4s: 'conteo_ruta_4sem'
metrics:
- 'min'

intervals: ['all']

## all, leave-one-out, leave-one-in, all-combinations
feature_group_strategies: ['leave-one-out']
#feature_group_strategies: ['all-combinations']

grid_config:
'sklearn.tree.DecisionTreeClassifier':
criterion: ['gini']
max_depth: [5,10,~]
min_samples_split: [10,50,100]
'sklearn.ensemble.RandomForestClassifier':
n_estimators: [200,300]
criterion: ['gini']
max_depth: [5,10]
max_features: ['sqrt']
min_samples_split: [10,50]
'triage.component.catwalk.estimators.classifiers.ScaledLogisticRegression':
penalty: ['l1','l2']
C: [0.01, 0.1, 1.0, 10]
'sklearn.dummy.DummyClassifier':
strategy: ['stratified']
'sklearn.ensemble.ExtraTreesClassifier':
n_estimators: [500]
criterion: ['gini']
max_depth: [5,10]
max_features: ['sqrt']
min_samples_split: [50,100]
'triage.component.catwalk.baselines.rankers.BaselineRankMultiFeature':
rules:
- [{feature: 'ecvr_entity_id_all_peso_neto_max', low_value_high_score: False}]

scoring:
testing_metric_groups:
-
metrics: [precision@, recall@]
thresholds:
percentiles: [10, 20, 25, 30]
top_n: [1000, 1400, 1750, 2100]

training_metric_groups:
-
metrics: [precision@, recall@]
thresholds:
percentiles: [10, 20, 25, 30]
top_n: [1000, 1400, 1750, 2100]

```

All the presented code worked fine until I used the `feature_group_strategies` in `leave-one-out` or `leave-one-in`. In both cases, I get the same error (detailed below). However, when I use `feature_group_strategies: ['all-combinations']`, it works, but it doesn't group the variables as expected, and I get results as if I were using `all`.

**Command used**:
```bash
triage experiment triage/experimentos_arribo/e2_ --n-db-processes 3 --n-processes 8 --no-validate --no-save-predictions
```

Everything runs smoothly until the matrix building step, where I encounter this error:

```
2024-09-08 15:17:14 - ERROR Child error
Traceback (most recent call last):
File "/Ccd/-pyenv/versions/tri-hp/lib/python3.10/site-packages/triage/experiments/multicore.py", line 166, in run_task_with_splatted_arguments return task_runner(**task)
File "/Ccd/pyenv/versions/tri-hp/lib/python3.10/site-packages/triage/component/architect/builders.py", line 321, in build_matrix
output, labels = self.stitch_csvs(feature_queries, label_query, matrix_store, matrix_uuid)
File "/Ccd/pyenv/versions/tri-hp/lib/python3.10/site-packages/triage/component/architect/builders.py", line 551, in stitch_csvs
if len(df_pl.get_column('as_of_date').head(1)[0].split)) > 1:
File "/Ccd/.pyenv/versions/tri-hp/lib/python3.10/site-packages/polars/dataframe/frame.py", line 6128, in get_column return self[name]
exceptions.ColumnNotFoundError: as_of_date
```

It seems like the `as_of_date` column is missing or not properly generated during matrix building, specifically when using the `leave-one-out` or `leave-one-in` strategies.

I expected the `leave-one-out` strategy to group variables accordingly and generate matrices without this error, but instead, the process halts when it reaches matrix building. I checked the matrices generated in the process and confirmed that the as_of_date column is indeed present.

My questions would be:
- Is this a known issue with these feature grouping strategies?
- Could this be related to how the `as_of_date` column is handled with these strategies?

Any guidance or suggestions would be greatly appreciated!

Thank you for your help.